Real-time optimal protocol prediction of quantum key distribution using machine learning
International Journal of Pervasive Computing and Communications
ISSN: 1742-7371
Article publication date: 1 September 2022
Issue publication date: 16 November 2023
Abstract
Purpose
The purpose of optimal protocol prediction and the benefits offered by quantum key distribution (QKD), including unbreakable security, there is a growing interest in the practical realization of quantum communication. Realization of the optimal protocol predictor in quantum key distribution is a critical step toward commercialization of QKD.
Design/methodology/approach
The proposed work designs a machine learning model such as K-nearest neighbor algorithm, convolutional neural networks, decision tree (DT), support vector machine and random forest (RF) for optimal protocol selector for quantum key distribution network (QKDN).
Findings
Because of the effectiveness of machine learning methods in predicting effective solutions using data, these models will be the best optimal protocol selectors for achieving high efficiency for QKDN. The results show that the best machine learning method for predicting optimal protocol in QKD is the RF algorithm. It also validates the effectiveness of machine learning in optimal protocol selection.
Originality/value
The proposed work was done using algorithms like the local search algorithm or exhaustive traversal, however the major downside of using these algorithms is that it takes a very long time to revert back results, which is unacceptable for commercial systems. Hence, machine learning methods are proposed to see the effectiveness of prediction for achieving high efficiency.
Keywords
Citation
R., A., J.S., N. and Mondal, R. (2023), "Real-time optimal protocol prediction of quantum key distribution using machine learning", International Journal of Pervasive Computing and Communications, Vol. 19 No. 5, pp. 689-697. https://doi.org/10.1108/IJPCC-05-2022-0200
Publisher
:Emerald Publishing Limited
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